In This Article Gene Flow

  • Introduction
  • General Overviews
  • Famous Developers of Gene Flow Models
  • Direct versus Indirect Estimation of Gene Flow
  • Gene Flow versus Selection
  • Gene Flow through a Hybrid Zone
  • Gene Flow between Established Species
  • Simulating Genetic Data from Gene Flow Models
  • Directional Gene Flow
  • Effect of Unsampled Populations on Gene Flow

Evolutionary Biology Gene Flow
by
Peter Beerli
  • LAST MODIFIED: 26 May 2016
  • DOI: 10.1093/obo/9780199941728-0077

Introduction

In population genetics, both gene flow and migration assume that individuals arrive from other localities; gene flow also assumes that such individuals can successfully mate with locals and may produce offspring with mixed inheritance, whereas migration does not require potential mixing of genetic material. In practice, the terms migration and gene flow are equivalent in population genetics, although in other fields, such as ecology, migration may mean instead seasonal migration. This article treats gene flow and migration, as used in population genetics, equivalently. Discussions of gene flow started shortly after the definition of population genetics in the early 1900s, although the term itself first appeared in 1941. Its early usage was most commonly theoretical in nature because, except for breeding experiments, the inheritance of characters from parents to offspring was poorly understood: only the phenotypes were available for study. Technical advances between 1960 and 1980 allowed the characterization of enzyme variants (allozymes) that were treated as products of different genetic alleles. These techniques permitted the first studies of genetic variation within and between natural populations of plants and animals; and commonly used analyses during this time were based on allele frequencies. In 1982, a theoretical breakthrough arrived with the formal description of coalescence theory, which sparked development of many inference methods based on finite mutation models and complex population models. Since 1990, microsatellite and DNA sampling methods have replaced the original allozyme-based approaches. The improvement of detection of genetic differences led to a large number of new methods to estimate the magnitude of gene flow. Many fields in biology use the concept of gene flow routinely as a general characterization of the data gathered, leading to a large literature using measurements of gene flow and a considerable literature proposing new measures and comparisons of measures.

General Overviews

Slatkin 1985 reviewed the effects of gene flow on variability within populations. The basics of migration/gene flow are treated prominently in all population genetics textbooks. Chapters in Gillespie 2004, Hartl and Clark 1997, and Hedrick 2000 reviewed gene flow in terms of standard population genetics theory based on allele frequencies without delivering in-depth analysis of the topic. The online text Felsenstein 2013 introduced gene flow from a mathematical viewpoint and also included a more modern treatment of gene flow in the context of coalescence theory. Wakeley 2009 discussed gene flow exclusively in the coalescence framework. A considerable body of work is dedicated to the inference of gene flow, and several software packages exist. Kuhner 2009 reviewed coalescence-based software packages, whereas Excoffier and Heckel 2006 focused on allele frequency-based software.

  • Excoffier, L., and G. Heckel. 2006. Computer programs for population genetics data analysis: A survival guide. Nature Reviews Genetics 7.10: 745–758.

    DOI: 10.1038/nrg1904E-mail Citation »

    An overview of inference programs to estimate population genetics quantities. A large section is devoted to programs that infer the magnitude of gene flow. This review is mostly centered on allele-frequency based studies.

  • Felsenstein, J. 2013. Theoretical population genetics. Seattle, WA: Joe Felsenstein.

    E-mail Citation »

    This is a good reference text for the mathematically inclined student. It is a treasure trove for population genetics. Gene flow is covered in several chapters. Felsenstein updates this text regularly and also maintains a list of known errors on the same website.

  • Gillespie, J. H. 2004. Population genetics: A concise guide. Johns Hopkins Univ. Press.

    E-mail Citation »

    This short volume gives an overview of classical population genetics and discusses gene flow in one chapter. I consider this short book as one of the best primers for population genetics.

  • Hartl, D. L., and A. G. Clark. 1997. Principles of population genetics. Sunderland, MA: Sinauer.

    E-mail Citation »

    The standard textbook on population genetics, suitable for upper-level undergraduate students and graduate students, discussing among many other topics migration and gene flow. The text was updated in 2007.

  • Hedrick, P. W. 2000. Genetics of populations. Sudbury, MA: Jones and Bartlett.

    E-mail Citation »

    A commonly used population genetics textbook, used for undergraduate and graduate students education. Gene flow is discussed in depth in one of the chapters.

  • Kuhner, M. K. 2009. Coalescent genealogy samplers: Windows into population history. Trends in Ecology and Evolution 24.2: 86–93.

    DOI: 10.1016/j.tree.2008.09.007E-mail Citation »

    Description of inference programs that use coalescence theory to estimate population genetic parameters, including gene flow parameters. It focuses on the most commonly used programs at the time.

  • Slatkin, M. 1985. Gene flow in natural populations. Annual Review of Ecology and Systematics 16:393–430.

    DOI: 10.1146/annurev.es.16.110185.002141E-mail Citation »

    An overview of the literature discussing gene flow. This key review addressed the theoretical findings that are based on allele frequencies before 1985.

  • Wakeley, J. 2009. Coalescent theory: An introduction. Greenwood Village, CO: Roberts.

    E-mail Citation »

    A complete survey of coalescence theory. A few chapters discuss gene flow, but usually use approximations that are rarely used in the coalescence-based inference programs that estimate the magnitude of gene flow among populations.

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